Adherence to medication regimens continues to rank as a major clinical problem in disease management. Achieving optimal medication adherence requires patients being prescribed the right medication, filling it and taking it correctly over time. This requires appropriate prescribing, effective patient-provider communication, coordination among care-providers and active engagement and participation by patients. Poor adherence to medication regimens accounts for a substantial load on health care costs in the United States. Of all medication-related hospital admissions in the United States, 33 to 69 percent are due to poor medication adherence, costing more than $100 billion annually in increased medical costs.
There have been a number of efforts addressing systems or devices for medication adherence. For example, a context-aware pill bottle/stand that provided visual and audio alerts to take a medication on time was developed in A. Agarawala, S. Greenberg, and G. Ho, “The context-aware pill bottle and medication monitor,” Technical Report, Department of Computer Science, University of Calgary, Calgary, Canada, 2004. The system operated based on the limiting assumption that the pill was taken when a pill bottle was removed from the stand. A smart medication dispenser was proposed in J. Pak and K. Park, “Construction of a smart medication dispenser with high degree of scalability and remote manageability,” Journal of Biomedicine and Biotechnology, vol. 2012, 2012, which dispensed a predetermined medication at a predetermined time. Again, this device did not detect whether the user was actually taking the medication. Methods based on computer vision techniques have also appeared in H. H. Huynh, J. Meunier, J. Sequeira, and M. Daniel, “Real time detection, tracking and recognition of medication intake,” World Academy of Science, Engineering and Technology, vol. 60, pp. 280-287, 2009; G. Bilodeau and S. Ammouri, “Monitoring of medication intake using a camera system,” Journal of Medical Systems, vol. 35, no. 3, pp. 377-389, 2011; and F. Hasanuzzaman, X. Yang, Y. Tian, Q. Liu, and E. Capezuti, “Monitoring activity of taking medicine by incorporating RFID and video analysis,” Network Modeling Analysis in Health Informatics and Bioinformatics, pp. 1-10, 2013. Obviously, the limitation with such vision based systems is that they require the user to take a medication within the field of view of a camera and cannot monitor the user wherever the user goes. A system consisting of several sensors (motion sensor, wearable sensor and bed sensor) was proposed in J. Lundell, T. L. Hayes, S. Vurgun, U. Ozertem, J. Kimel, J. Kaye, F. Guilak, and M. Pavel, “Continuous activity monitoring and intelligent contextual prompting to improve medication adherence,” IEEE Proceedings of 29th Annual International Conference on Engineering in Medicine and Biology Society (EMBS), pp. 6286-6289, 2007, which was rather complex to set up and operate. Each of the above-noted papers is hereby incorporated by reference in their entireties.
The availability of a low-cost and easy-to-use device for monitoring medication adherence has been lacking. This disclosure generally relates to wearable medication adherence monitoring systems and methods.